social-media-intelligence

What this skill does

# Social media intelligence

Systematic approaches for monitoring, analyzing, and investigating social media for journalism.

## When to activate

- Tracking how a story spreads across platforms
- Investigating potential coordinated inauthentic behavior
- Monitoring breaking news across social platforms
- Analyzing account networks and relationships
- Detecting bot activity or manipulation campaigns
- Building evidence trails for digital investigations
- Archiving social content before deletion

## Real-time monitoring

### Multi-platform tracker

```python
from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Optional, Dict
from enum import Enum
import hashlib

class Platform(Enum):
    TWITTER = "twitter"  # X since 2023; "twitter" retained for legacy data
    FACEBOOK = "facebook"
    INSTAGRAM = "instagram"
    TIKTOK = "tiktok"
    YOUTUBE = "youtube"
    REDDIT = "reddit"
    THREADS = "threads"
    BLUESKY = "bluesky"
    MASTODON = "mastodon"
    TELEGRAM = "telegram"

@dataclass
class SocialPost:
    platform: Platform
    post_id: str
    author: str
    content: str
    timestamp: datetime
    url: str
    engagement: Dict[str, int] = field(default_factory=dict)
    media_urls: List[str] = field(default_factory=list)
    archived_urls: List[str] = field(default_factory=list)
    content_hash: str = ""

    def __post_init__(self):
        # Hash content for duplicate detection
        self.content_hash = hashlib.md5(
            f"{self.platform.value}:{self.content}".encode()
        ).hexdigest()

@dataclass
class MonitoringQuery:
    keywords: List[str]
    platforms: List[Platform]
    accounts: List[str] = field(default_factory=list)
    hashtags: List[str] = field(default_factory=list)
    exclude_terms: List[str] = field(default_factory=list)
    start_date: Optional[datetime] = None

    def to_search_string(self, platform: Platform) -> str:
        """Generate platform-specific search query."""
        parts = []

        # Keywords
        if self.keywords:
            parts.append(' OR '.join(f'"{k}"' for k in self.keywords))

        # Hashtags
        if self.hashtags:
            parts.append(' OR '.join(f'#{h}' for h in self.hashtags))

        # Exclusions
        if self.exclude_terms:
            parts.append(' '.join(f'-{t}' for t in self.exclude_terms))

        return ' '.join(parts)
```

### Breaking news monitor

```python
from collections import defaultdict
from datetime import datetime, timedelta

class BreakingNewsDetector:
    """Detect sudden spikes in keyword mentions."""

    def __init__(self, baseline_window_hours: int = 24):
        self.baseline_window = timedelta(hours=baseline_window_hours)
        self.mention_history = defaultdict(list)

    def add_mention(self, keyword: str, timestamp: datetime):
        """Record a mention of a keyword."""
        self.mention_history[keyword].append(timestamp)
        # Prune old data
        cutoff = datetime.now() - self.baseline_window * 2
        self.mention_history[keyword] = [
            t for t in self.mention_history[keyword] if t > cutoff
        ]

    def is_spiking(self, keyword: str, threshold_multiplier: float = 3.0) -> bool:
        """Check if keyword is spiking above baseline."""
        now = datetime.now()
        recent = sum(1 for t in self.mention_history[keyword]
                    if t > now - timedelta(hours=1))

        baseline_hourly = len([
            t for t in self.mention_history[keyword]
            if t > now - self.baseline_window
        ]) / self.baseline_window.total_seconds() * 3600

        if baseline_hourly == 0:
            return recent > 10  # Arbitrary threshold for new topics

        return recent > baseline_hourly * threshold_multiplier

    def get_trending(self, top_n: int = 10) -> List[tuple]:
        """Get keywords sorted by spike intensity."""
        spikes = []
        for keyword in self.mention_history:
            if self.is_spiking(keyword):
                recent = sum(1 for t in self.mention_history[keyword]
                           if t > datetime.now() - timedelta(hours=1))
                spikes.append((keyword, recent))

        return sorted(spikes, key=lambda x: x[1], reverse=True)[:top_n]
```

## Account analysis

### Authenticity indicators

```python
from dataclasses import dataclass
from datetime import datetime
from typing import List, Optional

@dataclass
class AccountAnalysis:
    username: str
    platform: Platform
    created_date: Optional[datetime] = None
    follower_count: int = 0
    following_count: int = 0
    post_count: int = 0

    # Authenticity signals
    profile_photo_is_stock: Optional[bool] = None
    bio_contains_keywords: List[str] = field(default_factory=list)
    posts_primarily_reshares: Optional[bool] = None
    posting_pattern_irregular: Optional[bool] = None
    engagement_ratio_suspicious: Optional[bool] = None

    def calculate_red_flags(self) -> dict:
        """Score account authenticity."""
        flags = {}

        # Account age
        if self.created_date:
            age_days = (datetime.now() - self.created_date).days
            if age_days < 30:
                flags['new_account'] = f"Created {age_days} days ago"

        # Follower ratio
        if self.following_count > 0:
            ratio = self.follower_count / self.following_count
            if ratio < 0.1:
                flags['low_follower_ratio'] = f"Ratio: {ratio:.2f}"

        # Posting frequency
        if self.created_date and self.post_count > 0:
            age_days = max(1, (datetime.now() - self.created_date).days)
            posts_per_day = self.post_count / age_days
            if posts_per_day > 50:
                flags['excessive_posting'] = f"{posts_per_day:.0f} posts/day"

        # Stock photo check
        if self.profile_photo_is_stock:
            flags['stock_profile_photo'] = "Profile appears to be stock image"

        return flags

    def authenticity_score(self) -> int:
        """0-100 score, higher = more likely authentic."""
        score = 100
        flags = self.calculate_red_flags()

        penalty_per_flag = 20
        score -= len(flags) * penalty_per_flag

        return max(0, score)
```

### Network mapping

```python
from collections import defaultdict
from typing import Set, Dict

class AccountNetwork:
    """Map relationships between accounts."""

    def __init__(self):
        self.interactions = defaultdict(lambda: defaultdict(int))
        self.accounts = {}

    def add_interaction(self, from_account: str, to_account: str,
                       interaction_type: str = "mention"):
        """Record an interaction between accounts."""
        self.interactions[from_account][to_account] += 1

    def find_clusters(self, min_interactions: int = 3) -> List[Set[str]]:
        """Find groups of accounts that frequently interact."""
        # Build adjacency with minimum threshold
        adjacency = defaultdict(set)
        for from_acc, targets in self.interactions.items():
            for to_acc, count in targets.items():
                if count >= min_interactions:
                    adjacency[from_acc].add(to_acc)
                    adjacency[to_acc].add(from_acc)

        # Find connected components
        visited = set()
        clusters = []

        for account in adjacency:
            if account in visited:
                continue

            cluster = set()
            stack = [account]

            while stack:
                current = stack.pop()
                if current in visited:
                    continue
                visited.add(current)
                cluster.add(current)
                stack.extend(adjacency[current] - visited)

            if len(cluster) > 1:
                clusters.append(cluster)

        return sorted(clusters, key=len, reverse=True)

    def coordination_score(self, accounts: Set[str]) -> float:
        """Score how coordinated a group of